AI & Machine Learning Integration

Recommendation Engines

Build recommendation systems using collaborative filtering, content-based filtering, or hybrid approaches. Provide personalized product or content recommendations with real-time updates and A/B testing.

Complexity: Complex 21-34 effort units 5-8 weeks

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Project Milestone & Feature Breakdown

3

Project Milestones

7

Features

29

Total Effort Units

1

Data Collection & Processing

Collect user behavior data

8 pts 1-2 weeks 2 Features

Event Tracking

3 pts Medium

Track views, clicks, purchases

User Profiles

5 pts Complex

Build user preference profiles

Deliverables

Event tracking
User profiles
Data pipeline

2

Recommendation Algorithms

Implement recommendation logic

13 pts 2-3 weeks 3 Features

Collaborative Filtering

5 pts Complex

User-user and item-item recommendations

Content-Based Filtering

5 pts Complex

Recommend based on item features

Hybrid Approach

3 pts Medium

Combine multiple recommendation strategies

Deliverables

Recommendation models
Scoring algorithms
Ranking system

3

Serving & Optimization

Real-time recommendations at scale

8 pts 1-2 weeks 2 Features

Real-Time Serving

5 pts Complex

Low-latency recommendation API

A/B Testing

3 pts Medium

Test different recommendation strategies

Deliverables

Recommendation API
Caching layer
A/B testing framework

Technical Stack

Python Scikit-learn TensorFlow Redis PostgreSQL FastAPI Spark

Key Considerations

Cold start problem

Data sparsity

Real-time vs batch processing

Scalability

Explainability

Success Criteria

Recommendations are relevant

Click-through rate improved

API latency under 100ms

System scales to millions of items

A/B tests show improvement

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